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Journal articleYao X, Lu X, Zhou Y, et al., 2023,
Rectifying interphases for preventing Li dendrite propagation in solid-state electrolytes
, ENERGY & ENVIRONMENTAL SCIENCE, Vol: 16, Pages: 2167-2176, ISSN: 1754-5692- Author Web Link
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- Citations: 2
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Journal articleSeminara L, Dosen S, Mastrogiovanni F, et al., 2023,
A hierarchical sensorimotor control framework for human-in-the-loop robotic hands.
, Science Robotics, Vol: 8, Pages: 1-8, ISSN: 2470-9476Human manual dexterity relies critically on touch. Robotic and prosthetic hands are much less dexterous and make little use of the many tactile sensors available. We propose a framework modeled on the hierarchical sensorimotor controllers of the nervous system to link sensing to action in human-in-the-loop, haptically enabled, artificial hands.
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Journal articleSu T, Calvo RA, Jouaiti M, et al., 2023,
Assessing a sleep interviewing chatbot to improve subjective and objective sleep: protocol for an observational feasibility study
, JMIR Research Protocols, Vol: 12, Pages: 1-10, ISSN: 1929-0748BACKGROUND: Sleep disorders are common among the aging population and people with neurodegenerative diseases. Sleep disorders have a strong bidirectional relationship with neurodegenerative diseases, where they accelerate and worsen one another. Although one-to-one individual cognitive behavioral interventions (conducted in-person or on the internet) have shown promise for significant improvements in sleep efficiency among adults, many may experience difficulties accessing interventions with sleep specialists, psychiatrists, or psychologists. Therefore, delivering sleep intervention through an automated chatbot platform may be an effective strategy to increase the accessibility and reach of sleep disorder intervention among the aging population and people with neurodegenerative diseases. OBJECTIVE: This work aims to (1) determine the feasibility and usability of an automated chatbot (named MotivSleep) that conducts sleep interviews to encourage the aging population to report behaviors that may affect their sleep, followed by providing personalized recommendations for better sleep based on participants' self-reported behaviors; (2) assess the self-reported sleep assessment changes before, during, and after using our automated sleep disturbance intervention chatbot; (3) assess the changes in objective sleep assessment recorded by a sleep tracking device before, during, and after using the automated chatbot MotivSleep. METHODS: We will recruit 30 older adult participants from West London for this pilot study. Each participant will have a sleep analyzer installed under their mattress. This contactless sleep monitoring device passively records movements, heart rate, and breathing rate while participants are in bed. In addition, each participant will use our proposed chatbot MotivSleep, accessible on WhatsApp, to describe their sleep and behaviors related to their sleep and receive personalized recommendations for better sleep tailored to their specific reasons for disrup
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Journal articleNiu Z, Zhao W, Wu B, et al., 2023,
π learning: a performance‐Informed framework for microstructural electrode design
, Advanced Energy Materials, Vol: 13, Pages: 1-14, ISSN: 1614-6832Designing high-performance porous electrodes is the key to next-generation electrochemical energy devices. Current machine-learning-based electrode design strategies are mainly orientated toward physical properties; however, the electrochemical performance is the ultimate design objective. Performance-orientated electrode design is challenging because the current data driven approaches do not accurately extract high-dimensional features in complex multiphase microstructures. Herein, this work reports a novel performance-informed deep learning framework, termed π learning, which enables performance-informed microstructure generation, toward overall performance prediction of candidate electrodes by adding most relevant physical features into the learning process. This is achieved by integrating physics-informed generative adversarial neural networks (GANs) with convolutional neural networks (CNNs) and with advanced multi-physics, multi-scale modeling of 3D porous electrodes. This work demonstrates the advantages of π learning by employing two popular design philosophies: forward and inverse designs, for the design of solid oxide fuel cells electrodes. π learning thus has the potential to unlock performance-driven learning in the design of next generation porous electrodes for advanced electrochemical energy devices such as fuel cells and batteries.
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Journal articleMiller JA, Veprek LH, Deterding S, et al., 2023,
Practical recommendations from a multi-perspective needs and challenges assessment of citizen science games
, PLOS ONE, Vol: 18, ISSN: 1932-6203 -
Journal articleBurge T, Jeffers J, Myant C, 2023,
A computational design of experiments based method for evaluation of off-the-shelf total knee replacement implants
, Computer Methods in Biomechanics and Biomedical Engineering, Vol: 26, Pages: 629-638, ISSN: 1025-5842A methodology to explore the design space of off-the-shelf total knee replacement implant designs is outlined. Generic femur component and tibia plate designs were scaled to thousands of sizes and virtually fitted to 244 test subjects. Various implant designs and sizing requirements between genders and ethnicities were evaluated. 5 sizes optimised via the methodology produced a good global fit for most subjects. However, clinically significant over/underhang was present in 19% of subjects for tibia plates and 25% for femur components, reducing to 11/20% with 8 sizes. The analysis highlighted subtly better fit performance was obtained using sizes with unequal spacing.
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Journal articleMacManus DB, Khorshidi MA, Ghajari M, et al., 2023,
Micromechanics in biology and medicine
, IET Nanobiotechnology, Vol: 17, Pages: 125-126, ISSN: 1751-8741 -
Journal articleEngel I, Daugintis R, Vicente T, et al., 2023,
The SONICOM HRTF dataset
, Journal of the Audio Engineering Society, Vol: 71, Pages: 241-253, ISSN: 0004-7554Immersive audio technologies, ranging from rendering spatialized sounds accurately to efficient room simulations, are vital to the success of augmented and virtual realities. To produce realistic sounds through headphones, the human body and head must both be taken into account. However, the measurement of the influence of the external human morphology on the sounds incoming to the ears, which is often referred to as head-related transfer function (HRTF), is expensive and time-consuming. Several datasets have been created over the years to help researcherswork on immersive audio; nevertheless, the number of individuals involved and amount of data collected is often insufficient for modern machine-learning approaches. Here, the SONICOM HRTF dataset is introduced to facilitate reproducible research in immersive audio. This dataset contains the HRTF of 120 subjects, as well as headphone transfer functions; 3D scans of ears, heads, and torsos; and depth pictures at different angles around subjects' heads.
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Journal articlePinson P, 2023,
What may future electricity markets look like?
, Journal of Modern Power Systems and Clean Energy, Vol: 11, Pages: 705-713, ISSN: 2196-5625Should the organization, design and functioning of electricity markets be taken for granted? Definitely not. While decades of evolution of electricity markets in countries that committed early to restructure their electric power sector made us believe that we may have found the right and future-proof model, the substantially and rapidly evolving context of our power and energy systems is challenging this idea in many ways. Actually, that situation brings both challenges and opportunities. Challenges include accommodation of renewable energy generation, decentralization and support to investment, while opportunities are mainly that advances in technical and social sciences provide us with many more options in terms of future market design. We here take a holistic point of view, by trying to understand where we are coming from with electricity markets and where we may be going. Future electricity markets should be made fit for purpose by considering them as a way to organize and operate a socio-techno-economic system.
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Journal articleHarkin R, Wu H, Nikam S, et al., 2023,
Evaluation of the role of hatch-spacing variation in a lack-of-fusion defect prediction criterion for laser-based powder bed fusion processes
, International Journal of Advanced Manufacturing Technology, Vol: 126, Pages: 659-673, ISSN: 0178-0026Lack of fusion (LOF) defects impact adversely on the mechanical properties of additively manufactured components produced via laser-based powder bed fusion. Following a stress-relieving heat treatment, the tensile properties and hardness of Ti6Al4V components were found to be negatively impacted by the presence of LOF defects. This work considers a geometrical-based inequality for the prediction of LOF defects. We critically evaluate an LOF criterion using both the experimentally and analytically obtained melt pool geometries. Experimentally, we determined melt pool dimensions by analysing a single-layer, multi-track deposition with oversized hatch spacing in order to establish depth and width from non-overlapping melt pools. Analytically, Rosenthal-based predictions of melt pool size (width and depth) are applied. To investigate LOF defects, we used hatch spacing as the main parameter variation to investigate defects while keeping all other controllable parameters unchanged. An original LOF criterion from the literature was found to be an adequate predictor of LOF defects when experimentally obtained melt pool geometry was used. Critically, however, the analytical expressions for melt pool geometry were found to be in error and this caused the LOF criterion to fail in predicting LOF defects in all cases where defects were observed experimentally. However, an adaptation to the LOF prediction criterion is proposed whereby it is recommended that a correction factor (or ) is used with the analytically derived melt pool geometry. Furthermore, this correction is extended into the laser power versus scanning speed operating space to give minimum (corrected) line energy for LOF avoidance in Ti6Al4V components.
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Journal articleBaker CE, Yu X, Patel S, et al., 2023,
A review of cyclist head injury, impact characteristics and the implications for helmet assessment methods
, Annals of Biomedical Engineering, Vol: 51, Pages: 875-904, ISSN: 0090-6964Head injuries are common for cyclists involved in collisions. Such collision scenarios result in a range of injuries, with different head impact speeds, angles, locations, or surfaces. A clear understanding of these collision characteristics is vital to design high fidelity test methods for evaluating the performance of helmets. We review literature detailing real-world cyclist collision scenarios and report on these key characteristics. Our review shows that helmeted cyclists have a considerable reduction in skull fracture and focal brain pathologies compared to non-helmeted cyclists, as well as a reduction in all brain pathologies. The considerable reduction in focal head pathologies is likely to be due to helmet standards mandating thresholds of linear acceleration. The less considerable reduction in diffuse brain injuries is likely to be due to the lack of monitoring head rotation in test methods. We performed a novel meta-analysis of the location of 1809 head impacts from ten studies. Most studies showed that the side and front regions are frequently impacted, with one large, contemporary study highlighting a high proportion of occipital impacts. Helmets frequently had impact locations low down near the rim line. The face is not well protected by most conventional bicycle helmets. Several papers determine head impact speed and angle from in-depth reconstructions and computer simulations. They report head impact speeds from 5 to 16 m/s, with a concentration around 5 to 8 m/s and higher speeds when there was another vehicle involved in the collision. Reported angles range from 10° to 80° to the normal, and are concentrated around 30°-50°. Our review also shows that in nearly 80% of the cases, the head impact is reported to be against a flat surface. This review highlights current gaps in data, and calls for more research and data to better inform improvements in testing methods of standards and rating schemes and raise helmet s
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Journal articleArrese-Igor M, Vong M, Orue A, et al., 2023,
Solid-state Li-ion batteries with carbon microfiber electrodes via 3D electrospinning
, APPLIED PHYSICS LETTERS, Vol: 122, ISSN: 0003-6951 -
Conference paperLaschke M, Bucher A, Coulton P, et al., 2023,
Moral Agents for Sustainable Transitions: Ethics, Politics, Design
Artificial moral agents - systems that engage in explicit moral reasoning on their own and with users - present a potential new paradigm for behavior and system change for social and environmental sustainability. Moral agents could replace current individualist, prescriptive, inflexible, and opaque interventions with systems that transparently state their values and then openly deliberate and contest these with users, or agents that represent human and non-human stakeholders such as future generations, species, or ecosystems. Indeed, moral agents could mark a genuine new form of more-than-human interactions and human-technology relation, where we relate to artificial systems as a counterpart. To jointly articulate key questions and possible futures around moral agents, this workshop convenes HCI, AI, behaviour change, and critical and speculative design researchers and practitioners.
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Journal articleRuan H, Barreras JV, Engstrom T, et al., 2023,
Lithium-ion battery lifetime extension: A review of derating methods
, Journal of Power Sources, Vol: 563, Pages: 1-17, ISSN: 0378-7753Extending lithium-ion battery lifetime is essential for mainstream uptake of electric vehicles. However, battery degradation is complex and involves coupling of underpinning electrochemical, thermal and mechanical processes, with behaviours varying based on chemistry, operating conditions and design. Derating is an attractive approach for extending lifetime due to ease of implementation, however, uncertainties remain around the optimal approach and their impacts. In this paper, we present a critical review of derating methods; dividing approaches into dynamic or static approaches based on whether the derated parameters changed with battery aging or not. Furthermore, we analyse and comment on approaches which are classified as being either heuristic or model-based. Analysis, comparison, and discussion around the derating sub-categories are presented towards highlighting underpinning insights of derating. Benefits and impacts of derating are quantified, and challenges with implementation are identified along with identification of research gaps, practical considerations and perspectives for future directions.
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Conference paperKooplikkattil Sadan M, 2023,
Singular Active-Inactive Mixture (SAIM) NMC cathode for lithium-ion batteries
, STFC Early Career Researchers Conference 2023 -
Journal articleBurge TA, Munford MJ, Kechagias S, et al., 2023,
Automating the customization of stiffness-matched knee implants using machine learning techniques
, The International Journal of Advanced Manufacturing Technology, ISSN: 0268-3768In knee arthroplasty, implants are used to replace the articulating surfaces of the tibia and femur bones, with most constituting of solid metallic components. Consequentially, biomechanical stresses and strains are no longer adequately distributed at the joint post-surgery, preventing beneficial bone remodeling. To mitigate this studies have explored additively manufacturing implants with porous lattice structures to match the mechanical properties of bone. Authors have also outlined how such structures can be designed using computed tomography data to simulate the stiffness of individuals’ bones. Such methods however currently require substantial manual work by trained professionals to process the image files, extract the density information, and design lattice structures. This study proposes what is believed to be the first fully automatic pipeline capable of producing tibial trays with compliant structures customized specifically for individuals’ bones, achieved using machine learning methods. The novel process, combining classification, object detection, and segmentation machine learning models, used to facilitate the automated workflow, is outlined. The efficaciousness of the pipeline is then demonstrated by testing it using clinical computed tomography data and comparing the results with those obtained manually. As a proof of concept, prototype designs generated by the pipeline with differing degrees of complexity, up to and including mapping stiffness variation in 3D through the shaft of the tibia, were also fabricated.
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Journal articleJenkinson G, Houghton N, van Zalk N, et al., 2023,
Acceptability of automated robotic clinical breast examination: survey study
, Journal of Participatory Medicine, Vol: 15, Pages: 1-16, ISSN: 2152-7202Background:In the United Kingdom, women aged 50 to 70 years are invited to undergo mammography. However, 10% of invasive breast cancers occur in women aged ≤45 years, representing an unmet need for young women. Identifying a suitable screening modality for this population is challenging; mammography is insufficiently sensitive, whereas alternative diagnostic methods are invasive or costly. Robotic clinical breast examination (R-CBE)—using soft robotic technology and machine learning for fully automated clinical breast examination—is a theoretically promising screening modality with early prototypes under development. Understanding the perspectives of potential users and partnering with patients in the design process from the outset is essential for ensuring the patient-centered design and implementation of this technology.Objective:This study investigated the attitudes and perspectives of women regarding the use of soft robotics and intelligent systems in breast cancer screening. It aimed to determine whether such technology is theoretically acceptable to potential users and identify aspects of the technology and implementation system that are priorities for patients, allowing these to be integrated into technology design.Methods:This study used a mixed methods design. We conducted a 30-minute web-based survey with 155 women in the United Kingdom. The survey comprised an overview of the proposed concept followed by 5 open-ended questions and 17 closed questions. Respondents were recruited through a web-based survey linked to the Cancer Research United Kingdom patient involvement opportunities web page and distributed through research networks’ mailing lists. Qualitative data generated via the open-ended questions were analyzed using thematic analysis. Quantitative data were analyzed using 2-sample Kolmogorov-Smirnov tests, 1-tailed t tests, and Pearson coefficients.Results:Most respondents (143/155, 92.3%) indicated that they would definitely or
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Journal articleSquires I, Dahari A, Cooper SJ, et al., 2023,
Artefact removal from micrographs with deep learning based inpainting
, Digital Discovery, Vol: 2, Pages: 316-326, ISSN: 2635-098XImaging is critical to the characterisation of materials. However, even with careful sample preparation and microscope calibration, imaging techniques can contain defects and unwanted artefacts. This is particularly problematic for applications where the micrograph is to be used for simulation or feature analysis, as artefacts are likely to lead to inaccurate results. Microstructural inpainting is a method to alleviate this problem by replacing artefacts with synthetic microstructure with matching boundaries. In this paper we introduce two methods that use generative adversarial networks to generate contiguous inpainted regions of arbitrary shape and size by learning the microstructural distribution from the unoccluded data. We find that one benefits from high speed and simplicity, whilst the other gives smoother boundaries at the inpainting border. We also describe an open-access graphical user interface that allows users to utilise these machine learning methods in a ‘no-code’ environment.
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Journal articleCaputo C, Cardin M-A, Ge P, et al., 2023,
Design and planning of flexible mobile Micro-Grids using Deep Reinforcement Learning
, Applied Energy, Vol: 335, ISSN: 0306-2619Ongoing risks from climate change have significantly impacted the livelihood of global nomadic communities and are likely to lead to increased migratory movements in coming years. As a result, mobility considerations are becoming increasingly important in energy systems planning, particularly to achieve energy access in developing countries. Advanced “Plug and Play” control strategies have been recently developed with such a decentralized framework in mind, allowing easier interconnection of nomadic communities, both to each other and to the main grid. Considering the above, the design and planning strategy of a mobile multi-energy supply system for a nomadic community is investigated in this work. Motivated by the scale and dimensionality of the associated uncertainties, impacting all major design and decision variables over the 30-year planning horizon, Deep Reinforcement Learning (DRL) Flexibility Analysis is implemented for the design and planning problem. DRL based solutions are benchmarked against several rigid baseline design options to compare expected performance under uncertainty. The results on a case study for ger communities in Mongolia suggest that mobile nomadic energy systems can be both technically and economically feasible, particularly when considering flexibility, although the degree of spatial dispersion among households is an important limiting factor. Additionally, the DRL based policies lead to the development of dynamic evolution and adaptability strategies, which can be used by the targeted communities under a very wide range of potential scenarios. Key economic, sustainability and resilience indicators such as Cost, Equivalent Emissions and Total Unmet Load are measured, suggesting potential improvements compared to available baselines of up to 25%, 67% and 76%, respectively. Finally, the decomposition of values of flexibility and plug and play operation is presented using a variation of real options theory, with important impl
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Journal articleWang J, Yang K, Sun S, et al., 2023,
Advances in thermal-related analysis techniques for solid-state lithium batteries
, INFOMAT, Vol: 5- Author Web Link
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- Citations: 4
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Journal articleWang J, Pinson P, Chatzivasileiadis S, et al., 2023,
On machine learning-based techniques for future sustainable and resilient energy systems
, IEEE Transactions on Sustainable Energy, Vol: 14, Pages: 1230-1243, ISSN: 1949-3029Permanently increasing penetration of converter-interfaced generation and renewable energy sources (RESs) makes modern electrical power systems more vulnerable to low probability and high impact events, such as extreme weather, which could lead to severe contingencies, even blackouts. These contingencies can be further propagated to neighboring energy systems over coupling components/technologies and consequently negatively influence the entire multi-energy system (MES) (such as gas, heating and electricity) operation and its resilience. In recent years, machine learning-based techniques (MLBTs) have been intensively applied to solve various power system problems, including system planning, or security and reliability assessment. This paper aims to review MES resilience quantification methods and the application of MLBTs to assess the resilience level of future sustainable energy systems. The open research questions are identified and discussed, whereas the future research directions are identified.
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Journal articleMohammed AA, Miao J, Ragaisyte I, et al., 2023,
3D printed superparamagnetic stimuli-responsive starfish-shaped hydrogels
, Heliyon, Vol: 9, Pages: 1-13, ISSN: 2405-8440Magnetic-stimuli responsive hydrogels are quickly becoming a promising class of materials across numerous fields, including biomedical devices, soft robotic actuators, and wearable electronics. Hydrogels are commonly fabricated by conventional methods that limit the potential for complex architectures normally required for rapidly changing custom configurations. Rapid prototyping using 3D printing provides a solution for this. Previous work has shown successful extrusion 3D printing of magnetic hydrogels; however, extrusion-based printing is limited by nozzle resolution and ink viscosity. VAT photopolymerization offers a higher control over resolution and build-architecture. Liquid photo-resins with magnetic nanocomposites normally suffer from nanoparticle agglomeration due to local magnetic fields. In this work, we develop an optimised method for homogenously infusing up to 2 wt % superparamagnetic iron oxide nanoparticles (SPIONs) with a 10 nm diameter into a photo-resin composed of water, acrylamide and PEGDA, with improved nanoparticle homogeneity and reduced agglomeration during printing. The 3D printed starfish hydrogels exhibited high mechanical stability and robust mechanical properties with a maximum Youngs modulus of 1.8 MPa and limited shape deformation of 10% when swollen. Each individual arm of the starfish could be magnetically actuated when a remote magnetic field is applied. The starfish could grab onto a magnet with all arms when a central magnetic field was applied. Ultimately, these hydrogels retained their shape post-printing and returned to their original formation once the magnetic field had been removed. These hydrogels can be used across a wide range of applications, including soft robotics and magnetically stimulated actuators.
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Journal articleZendle D, Flick C, Deterding S, et al., 2023,
The Many Faces of Monetisation: Understanding the Diversity and Extremity of Player Spending in Mobile Games via Massive-scale Transactional Analysis
, Games: Research and Practice, Vol: 1, Pages: 1-28<jats:p>With the rise of microtransactions, particularly in the mobile games industry, there has been ongoing concern that games reliant on these obtain substantial revenue from a small proportion of heavily involved individuals, to an extent that may be financially burdensome to these individuals. Yet despite substantive grey literature and speculation on this topic, there is little robust data available. We explore the revenue distribution in microtransaction-based mobile games using a transactional dataset of $4.7B in in-game spending drawn from 69,144,363 players of 2,873 mobile games over the course of 624 days. We find diverse revenue distributions in mobile games, ranging from a “uniform” cluster, in which all spenders invest approximately similar amounts, to “hyper-Pareto” games, in which a large proportion of revenue (approximately 38%) stems from 1% of spenders alone. Specific kinds of games are typified by higher spending: The more a game relies on its top 1% for revenue generation, the more these individuals tend to spend, with simulated gambling products (“social casinos”) at the top. We find a small subset of games across all genres, clusters, and age ratings in which the top 1% of gamers are highly financially involved—spending an average of $66,285 each in the 624 days under evaluation in the most extreme case. We discuss implications for future studies on links between gaming and wellbeing.</jats:p>
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Journal articleDeterding S, Mitchell K, Kowert R, et al., 2023,
Games Futures I
, Games: Research and Practice, Vol: 1, Pages: 1-4<jats:p>Games Futures collect short opinion pieces by industry and research veterans and new voices envisioning possible and desirable futures and needs for games and playable media. This inaugural series features eight of over thirty pieces.</jats:p>
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Journal articleDeterding S, Mitchell K, Kowert R, et al., 2023,
Inaugural Editorial: A Lighthouse for Games and Playable Media
, Games: Research and Practice, Vol: 1, Pages: 1-9<jats:p> In games and playable media, almost nothing is as it was at the turn of the millennium. Digital and analog games have exploded in reach, diversity, and relevance. Digital platforms and globalisation have shifted and fragmented their centres of gravity and how they are made and played. Games are converging with other media, technologies, and arts into a wide field of playable media. Games research has similarly exploded in volume and fragmented into disciplinary specialisms. All this can be deeply disorienting. The journal <jats:italic>Games: Research and Practice</jats:italic> wants to offer a lighthouse that helps readers orient themselves in this new, ever-shifting reality of games industry and games research. </jats:p>
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Journal articleYu Z, Sadati H, Perera S, et al., 2023,
Tapered whisker reservoir computing for real-time terrain identification-based navigation
, Scientific Reports, Vol: 13, Pages: 1-13, ISSN: 2045-2322This paper proposes a new method for real-time terrain recognition-based navigation for mobile robots. Mobile robots performing tasks in unstructured environments need to adapt their trajectories in real-time to achieve safe and efficient navigation in complex terrains. However, current methods largely depend on visual and IMU (inertial measurement units) that demand high computational resources for real-time applications. In this paper, a real-time terrain identification-based navigation method is proposed using an on-board tapered whisker-based reservoir computing system. The nonlinear dynamic response of the tapered whisker was investigated in various analytical and Finite Element Analysis frameworks to demonstrate its reservoir computing capabilities. Numerical simulations and experiments were cross-checked with each other to verify that whisker sensors can separate different frequency signals directly in the time domain and demonstrate the computational superiority of the proposed system, and that different whisker axis locations and motion velocities provide variable dynamical response information. Terrain surface-following experiments demonstrated that our system could accurately identify changes in the terrain in real-time and adjust its trajectory to stay on specific terrain.
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Journal articleYu X, Baker C, Brown M, et al., 2023,
In-depth bicycle collision reconstruction: from a crash helmet to brain injury evaluation
, Bioengineering, Vol: 10, Pages: 1-16, ISSN: 2306-5354Traumatic brain injury (TBI) is a prevalent injury among cyclists experiencing head collisions. In legal cases, reliable brain injury evaluation can be difficult and controversial as mild injuries cannot be diagnosed with conventional brain imaging methods. In such cases, accident reconstruction may be used to predict the risk of TBI. However, lack of collision details can render accident reconstruction nearly impossible. Here, we introduce a reconstruction method to evaluate the brain injury in a bicycle–vehicle collision using the crash helmet alone. Following a thorough inspection of the cyclist’s helmet, we identified a severe impact, a moderate impact and several scrapes, which helped us to determine the impact conditions. We used our helmet test rig and intact helmets identical to the cyclist’s helmet to replicate the damage seen on the cyclist’s helmet involved in the real-world collision. We performed both linear and oblique impacts, measured the translational and rotational kinematics of the head and predicted the strain and the strain rate across the brain using a computational head model. Our results proved the hypothesis that the cyclist sustained a severe impact followed by a moderate impact on the road surface. The estimated head accelerations and velocity (167 g, 40.7 rad/s and 13.2 krad/s2) and the brain strain and strain rate (0.541 and 415/s) confirmed that the severe impact was large enough to produce mild to moderate TBI. The method introduced in this study can guide future accident reconstructions, allowing for the evaluation of TBI using the crash helmet only.
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Journal articlePinson P, 2023,
Distributionally robust trading strategies for renewable energy producers
, IEEE Transactions on Energy Markets, Policy and Regulation, Vol: 1, Pages: 37-47Renewable energy generation is offered through electricity markets, quite some time in advance. This then leads to a problem of decision-making under uncertainty, which may be seen as a newsvendor problem. Contrarily to the conventional case for which underage and overage penalties are known, such penalties in the case of electricity markets are unknown, and difficult to estimate. In addition, one is actually only penalized for either overage or underage, not both. Consequently, we look at a slightly different form of a newsvendor problem, for a price-taker participant offering in electricity markets, which we refer to as Bernoulli newsvendor problem. After showing that its solution is consistent with that for the classical newsvendor problem, we then introduce distributionally robust versions, with ambiguity possibly about both the probabilistic forecasts for power generation and the chance of success of the Bernoulli variable. Both versions of the distributionally robust Bernoulli newsvendor problem admit closed-form solutions. We finally use simulation studies, as well as a real-world case-study application, to illustrate the workings and benefits from the approach.
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Journal articleFu Y, Yang K, Xue S, et al., 2023,
Surface Defects Reinforced Polymer-Ceramic Interfacial Anchoring for High-Rate Flexible Solid-State Batteries
, ADVANCED FUNCTIONAL MATERIALS, Vol: 33, ISSN: 1616-301X- Author Web Link
- Cite
- Citations: 3
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Journal articleLabazanova L, Peng S, Qiu L, et al., 2023,
Self-Reconfigurable Soft-Rigid Mobile Agent With Variable Stiffness and Adaptive Morphology
, IEEE ROBOTICS AND AUTOMATION LETTERS, Vol: 8, Pages: 1643-1650, ISSN: 2377-3766
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